The main theme of the research project is to understand the structural principles that govern sequence to structure relationships in proteins and binding of various ligands to proteins. These structural principles are being used to develop novel knowledge based computational methods for prediction of substrate specificity of proteins and also for predicting structural folds for proteins in absence of detectable sequence homology. Such computational tools can assign specific functions to various putative proteins in genomes and hence help in identification of novel metabolic pathways and protein interaction networks in newly sequenced genomes.

Substrate specificity of biosynthetic proteins & identification of novel metabolic pathwaysPrediction of substrate specificity of proteins has been a difficult task due to the lack of suitable scoring functions for accurate evaluation of protein-ligand interactions. Recent work from our laboratory has demonstrated that reliable prediction of substrate specificity can be carried out by combining evolutionary information from large number of proteins of known substrates, with structural information on active site geometry from homologous crystal structures. This work has provided a computational framework for correlating chemical structures of the polyketide or nonribosomal peptide products to the sequences of their respective megasynthases consisting of a variety of catalytic domains. Collaborative experimental work has demonstrated that these computational methods are not only useful in identification of biosynthetic products of uncharacterized proteins in newly sequenced genomes, they can also be used for rational design of novel natural products.

We are currently extending such predictions of structural fold and substrate specificity to other enzyme families with the objective of developing powerful computational methods for correlating sequence of other classes of biosynthetic proteins to their metabolic products, not only in prokaryotes but also in eukaryotes. Similar computational approach is also being used to analyze enzymes associated with novel post translational modifications like AMPylation, Eliminylation etc.

Prediction of protein-peptide interaction & identification of protein interaction networksOur research group is also investigating whether knowledge based structural bioinformatics approaches can be used for identifying protein interaction networks mediated by peptide recognition modules (PRMs) like kinases, SH3, SH2, WW, PDZ etc. Even though, a number of computational methods have been developed for identifying interaction partners of various PRMs, most of these studies have used a sequence based approach and have identified interaction partners based on motifs/profiles obtained from sequence analysis of a known set of experimentally identified interaction partners. Thus the applicability of such training based methods is limited to only those families of PRMs for which experimental substrate data is available. Therefore it is necessary to develop structure based approaches, which do not require training using experimental substrate data. We have developed a knowledge based computational protocol for modeling structures of protein-peptide complexes and used this protocol to formulate a novel structure based approach for predicting substrates of protein kinases and MHC proteins. The success of our structure based approach for protein kinases and MHCs encouraged us to develop similar structure based multi-scale approaches for deciphering substrate specificities of other PRMs like PDZ, WW, SH2, SH3 etc.

Systems biology approach for identification of conserved network modules & deciphering their dynamic features Apart from the approach of predicting metabolic and signaling networks based on substrate specificity or function of the component proteins, we also plan to use the systems biology approach of deciphering novel metabolic, signaling and transcriptional networks based on identification of conserved network motifs/modules and understanding their dynamic features.

Awards / Fellowships

National Bioscience Career Development Award (2009)

Fellow of Indian National Science Academy (Elected in 2013)

Fellow of Indian Academy of Science (Elected in 2012)

Fellow of The National Academy of Sciences, India, Allahabad (Elected in 2008)